Dataset of extremely low-dimensional images for PCA
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I am looking for a public data-set of images that differ from each other only slightly, so that after applying PCA they can be reconstructed with a small error from very few PCA coefficients. It can be any type of images, the purpose is only to demonstrate an extreme example of PCA.
machine-learning dataset pca
edited Jan 23 '18 at 12:27
Vaalizaadeh
7,61562265
asked Jan 23 '18 at 9:56
elliotpelliotp
111
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bumped to the homepage by Community♦ 2 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
I am looking for a public data-set of images that differ from each other only slightly, so that after applying PCA they can be reconstructed with a small error from very few PCA coefficients. It can be any type of images, the purpose is only to demonstrate an extreme example of PCA.
machine-learning dataset pca
edited Jan 23 '18 at 12:27
Vaalizaadeh
7,61562265
asked Jan 23 '18 at 9:56
elliotpelliotp
111
$endgroup$
bumped to the homepage by Community♦ 2 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
I am looking for a public data-set of images that differ from each other only slightly, so that after applying PCA they can be reconstructed with a small error from very few PCA coefficients. It can be any type of images, the purpose is only to demonstrate an extreme example of PCA.
machine-learning dataset pca
edited Jan 23 '18 at 12:27
Vaalizaadeh
7,61562265
asked Jan 23 '18 at 9:56
elliotpelliotp
111
$endgroup$
I am looking for a public data-set of images that differ from each other only slightly, so that after applying PCA they can be reconstructed with a small error from very few PCA coefficients. It can be any type of images, the purpose is only to demonstrate an extreme example of PCA.
machine-learning dataset pca
machine-learning dataset pca
edited Jan 23 '18 at 12:27
Vaalizaadeh
7,61562265
asked Jan 23 '18 at 9:56
elliotpelliotp
111
edited Jan 23 '18 at 12:27
Vaalizaadeh
7,61562265
asked Jan 23 '18 at 9:56
elliotpelliotp
111
edited Jan 23 '18 at 12:27
Vaalizaadeh
7,61562265
edited Jan 23 '18 at 12:27
Vaalizaadeh
7,61562265
edited Jan 23 '18 at 12:27
Vaalizaadeh
7,61562265
7,61562265
asked Jan 23 '18 at 9:56
elliotpelliotp
111
asked Jan 23 '18 at 9:56
elliotpelliotp
111
asked Jan 23 '18 at 9:56
elliotpelliotp
111
111
bumped to the homepage by Community♦ 2 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ 2 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
add a comment |
1 Answer
1
active
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Actually in your case I guess the pure images are not that important. The features that you extract from them are important because if your feature space is constructed base on intensity of images at different picture elements, pixels, then you will need so many coefficients. As an easy solution, use MNIST digits and use shape features to extract features from the images of numbers. You can use plausible number of features and then use PCA for the data that is in the new feature space that you have just constructed. In this case smaller number of coefficients will be needed if the features are fine.
answered Jan 23 '18 at 11:17
VaalizaadehVaalizaadeh
7,61562265
$endgroup$
$begingroup$
Thanks but as I said, I want to demonstrate PCA for the actual pixels of images.
$endgroup$
– elliotp
Jan 23 '18 at 12:16
$begingroup$
@elliotp You can use mnist to do so as well but I'm not sure how many coefficients will suffice for you purpose. I guess MNIST suites for your task because most of the numbers are centered, consequently center pixels with each other and marginal pixels with each other will have high correlation which may cause first principal components have great eigenvalues.
$endgroup$
– Vaalizaadeh
Jan 23 '18 at 12:26
$begingroup$
@elliotp also as a suggestion, I recommend you to pick sample of images of two different labels and plot the three eigenvalues with the greatest amounts.
$endgroup$
– Vaalizaadeh
Jan 23 '18 at 12:48
add a comment |
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1 Answer
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1 Answer
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oldest
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$begingroup$
Actually in your case I guess the pure images are not that important. The features that you extract from them are important because if your feature space is constructed base on intensity of images at different picture elements, pixels, then you will need so many coefficients. As an easy solution, use MNIST digits and use shape features to extract features from the images of numbers. You can use plausible number of features and then use PCA for the data that is in the new feature space that you have just constructed. In this case smaller number of coefficients will be needed if the features are fine.
answered Jan 23 '18 at 11:17
VaalizaadehVaalizaadeh
7,61562265
$endgroup$
$begingroup$
Thanks but as I said, I want to demonstrate PCA for the actual pixels of images.
$endgroup$
– elliotp
Jan 23 '18 at 12:16
$begingroup$
@elliotp You can use mnist to do so as well but I'm not sure how many coefficients will suffice for you purpose. I guess MNIST suites for your task because most of the numbers are centered, consequently center pixels with each other and marginal pixels with each other will have high correlation which may cause first principal components have great eigenvalues.
$endgroup$
– Vaalizaadeh
Jan 23 '18 at 12:26
$begingroup$
@elliotp also as a suggestion, I recommend you to pick sample of images of two different labels and plot the three eigenvalues with the greatest amounts.
$endgroup$
– Vaalizaadeh
Jan 23 '18 at 12:48
add a comment |
$begingroup$
Actually in your case I guess the pure images are not that important. The features that you extract from them are important because if your feature space is constructed base on intensity of images at different picture elements, pixels, then you will need so many coefficients. As an easy solution, use MNIST digits and use shape features to extract features from the images of numbers. You can use plausible number of features and then use PCA for the data that is in the new feature space that you have just constructed. In this case smaller number of coefficients will be needed if the features are fine.
answered Jan 23 '18 at 11:17
VaalizaadehVaalizaadeh
7,61562265
$endgroup$
$begingroup$
Thanks but as I said, I want to demonstrate PCA for the actual pixels of images.
$endgroup$
– elliotp
Jan 23 '18 at 12:16
$begingroup$
@elliotp You can use mnist to do so as well but I'm not sure how many coefficients will suffice for you purpose. I guess MNIST suites for your task because most of the numbers are centered, consequently center pixels with each other and marginal pixels with each other will have high correlation which may cause first principal components have great eigenvalues.
$endgroup$
– Vaalizaadeh
Jan 23 '18 at 12:26
$begingroup$
@elliotp also as a suggestion, I recommend you to pick sample of images of two different labels and plot the three eigenvalues with the greatest amounts.
$endgroup$
– Vaalizaadeh
Jan 23 '18 at 12:48
add a comment |
$begingroup$
Actually in your case I guess the pure images are not that important. The features that you extract from them are important because if your feature space is constructed base on intensity of images at different picture elements, pixels, then you will need so many coefficients. As an easy solution, use MNIST digits and use shape features to extract features from the images of numbers. You can use plausible number of features and then use PCA for the data that is in the new feature space that you have just constructed. In this case smaller number of coefficients will be needed if the features are fine.
answered Jan 23 '18 at 11:17
VaalizaadehVaalizaadeh
7,61562265
$endgroup$
Actually in your case I guess the pure images are not that important. The features that you extract from them are important because if your feature space is constructed base on intensity of images at different picture elements, pixels, then you will need so many coefficients. As an easy solution, use MNIST digits and use shape features to extract features from the images of numbers. You can use plausible number of features and then use PCA for the data that is in the new feature space that you have just constructed. In this case smaller number of coefficients will be needed if the features are fine.
answered Jan 23 '18 at 11:17
VaalizaadehVaalizaadeh
7,61562265
edited Jan 23 '18 at 11:22
answered Jan 23 '18 at 11:17
VaalizaadehVaalizaadeh
7,61562265
answered Jan 23 '18 at 11:17
VaalizaadehVaalizaadeh
7,61562265
answered Jan 23 '18 at 11:17
VaalizaadehVaalizaadeh
7,61562265
7,61562265
$begingroup$
Thanks but as I said, I want to demonstrate PCA for the actual pixels of images.
$endgroup$
– elliotp
Jan 23 '18 at 12:16
$begingroup$
@elliotp You can use mnist to do so as well but I'm not sure how many coefficients will suffice for you purpose. I guess MNIST suites for your task because most of the numbers are centered, consequently center pixels with each other and marginal pixels with each other will have high correlation which may cause first principal components have great eigenvalues.
$endgroup$
– Vaalizaadeh
Jan 23 '18 at 12:26
$begingroup$
@elliotp also as a suggestion, I recommend you to pick sample of images of two different labels and plot the three eigenvalues with the greatest amounts.
$endgroup$
– Vaalizaadeh
Jan 23 '18 at 12:48
add a comment |
$begingroup$
Thanks but as I said, I want to demonstrate PCA for the actual pixels of images.
$endgroup$
– elliotp
Jan 23 '18 at 12:16
$begingroup$
@elliotp You can use mnist to do so as well but I'm not sure how many coefficients will suffice for you purpose. I guess MNIST suites for your task because most of the numbers are centered, consequently center pixels with each other and marginal pixels with each other will have high correlation which may cause first principal components have great eigenvalues.
$endgroup$
– Vaalizaadeh
Jan 23 '18 at 12:26
$begingroup$
@elliotp also as a suggestion, I recommend you to pick sample of images of two different labels and plot the three eigenvalues with the greatest amounts.
$endgroup$
– Vaalizaadeh
Jan 23 '18 at 12:48
$begingroup$
Thanks but as I said, I want to demonstrate PCA for the actual pixels of images.
$endgroup$
– elliotp
Jan 23 '18 at 12:16
$begingroup$
Thanks but as I said, I want to demonstrate PCA for the actual pixels of images.
$endgroup$
– elliotp
Jan 23 '18 at 12:16
$begingroup$
@elliotp You can use mnist to do so as well but I'm not sure how many coefficients will suffice for you purpose. I guess MNIST suites for your task because most of the numbers are centered, consequently center pixels with each other and marginal pixels with each other will have high correlation which may cause first principal components have great eigenvalues.
$endgroup$
– Vaalizaadeh
Jan 23 '18 at 12:26
$begingroup$
@elliotp You can use mnist to do so as well but I'm not sure how many coefficients will suffice for you purpose. I guess MNIST suites for your task because most of the numbers are centered, consequently center pixels with each other and marginal pixels with each other will have high correlation which may cause first principal components have great eigenvalues.
$endgroup$
– Vaalizaadeh
Jan 23 '18 at 12:26
$begingroup$
@elliotp also as a suggestion, I recommend you to pick sample of images of two different labels and plot the three eigenvalues with the greatest amounts.
$endgroup$
– Vaalizaadeh
Jan 23 '18 at 12:48
$begingroup$
@elliotp also as a suggestion, I recommend you to pick sample of images of two different labels and plot the three eigenvalues with the greatest amounts.
$endgroup$
– Vaalizaadeh
Jan 23 '18 at 12:48
add a comment |
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